Electricity generating and distributing utilities must be able to generate power sufficient to serve their customers' peak energy demand. It is well understood by those in the industry that the power required to meet customers' peak energy demand is the most expensive energy to produce. When the load on an electrical system approaches the maximum generating capacity, utilities must either find additional supplies of energy or find ways to reduce the load; otherwise, blackouts or other outages may occur.
Utilities often use demand-response programs to balance the supply of electricity on their network with the electrical load demand by adjusting or controlling the demanded load rather than controlling the power station output. Load shedding is one example of demand response wherein a utility may reduce demand by controlling the output of high-energy-usage loads, typically devices such as air conditioners, hot water heaters, pool heaters, and the like. By shedding load during peak demand periods, peak load demand is shifted to off-peak periods, thereby “smoothing” short-term peaks in load demand. Effective load shedding provides numerous benefits, including reduced energy cost and improved electrical supply reliability, among others.
Load shedding may be accomplished through the use of a communicative controller cooperating with a device, such as a relay, that interrupts power to the load based on commands from the utility company. Such controllers are well known in the art as load-control receivers (“LCRs”) or load-control switches (“LCSs”).
Load shedding is most effective in managing the duty cycle of cyclic loads. For example, an air conditioner may cycle on and off every fifteen minutes to maintain a constant building temperature. That duty cycle can be altered (by a utility company via an LCS, for example) to reduce energy usage without causing uncomfortable changes in room temperature. During a demand-response event, a utility master station may command the LCSs to turn off or otherwise alter their duty cycle in order to reduce peak power demand below the electrical supply.
A problem with current demand-response systems is that they are unable to accurately predict current and future demand, and therefore how much demanded load will be available to be shed at the start of a demand-response event. This available load is known by those having skill in the art as the “demand reserve.” Accordingly, utilities generally use a rule-of-thumb guess to determine the proportion of units that can be cycled off, and for what duty cycle, sufficient to achieve the desired demand reduction.
Some prior art methods and devices have attempted to estimate demand reserve by considering weather information, historical data, and population statistics, among other parameters. For example, a group of residential air conditioners may be assumed to each be 1 kW of load (demand) at maximum, though such an assumed load may be de-rated based upon temperature. At temperatures greater than 95° F. one might assume 100% demand, and at 75° F. one might assume 10% demand. Relative humidity and cloud cover can also be considered in the prediction algorithm. These methods require periodic verification and the final computed population totals typically require a large degree of tolerance for decision making. Furthermore, such algorithms burden the master station software with large databases and/or complex computational requirements.
Other methods rely on gathering vast amounts of detailed data from the actual energy-consuming devices, then calculating real-time usage at a centralized server. Rather than relying on predictive, sampled data, such methods and devices, including those described in U.S. Pat. Nos. 8,032,233, 8,010,812, and 7,715,951, and related patent applications, to Forbes, Jr., et al., avoid statistical prediction techniques, and record actual energy usage of all energy-consuming devices in a region. The real-time data from the collective devices in a geographic region are transmitted to a centralized database for analysis, actual energy usage is calculated, and transmitted to a utility to use for load-shedding purposes. As such, actual demand reserve at any given time is known.
However, because such centralized demand reserve determining systems require that all energy-consuming devices report real-time data on a constant basis to a centralized database, extensive and complex infrastructure is required, including significant transmission bandwidth.